In the case of a comparison with a static reference vector, however, no account is taken of a gradual change of the user's biometric property which may result e.g. from aging processes, physical and psychological conditions of the user, diseases and the like. Likewise, using one single reference vector cannot cover the normal complexity of a biometric feature space, since e.g. non-linear dependencies between feature vectors to be accepted and feature vectors to be rejected cannot be modeled.
So as to still achieve acceptable recognition rates despite these disadvantages, often context-dependent (i.e. user-dependent) knowledge about the concrete feature space is integrated into the comparison or recognition operation (the so-called classifier), which on the one hand can lead to better recognition rates, but on the other hand makes the classification computing-intensive and inflexible. For this reason, conventional recognition methods on a portable data carrier provided with only limited resources often cannot produce the recognition rates required by security applications, because they lead to an increased number of misclassifications at least after some time due to the mentioned limitation and inflexibility of the feature vector comparison.
In this context DE 10 2004 043 875 A1 discloses a possibility to compensate the natural changes of biometric features by taking them into account using adaptation values when evaluating biometric features, e.g. by normalizing them. This ultimately influences parameters of the feature recognition, for example the recognition-/admission tolerance of the recognition method or parameters of the feature sensors. In contrast, WO 02/071314 A1 proposes to take into account the natural changes of biometric features by using a recognized biometric comparison template under certain conditions as a reference template for the next comparison template to be recognized. Finally, WO 2006/069158 A2 discloses a self-adaptive method for a biometric recognition, whereby the biometric reference data used in each case are selected from various biometric modalities with different weightings in dependence on security requirements and the biometric data quality. In so doing, the reference data can be continuously adjusted in a predetermined fashion by the comparative data last checked in each case.
It is the object of the present invention to provide a flexible and efficient biometric recognition which reliably compensates the natural changes of biometric features.